Combining publicly available Google Street View (GSV) images with deep learning and radiative transfer models enables researchers to assess urban physical and thermal environments, especially in high-density areas. This book introduces this advanced approach, which provides large-scale, high-accuracy, and high-density measurements of street-level urban featureskey to understanding urban radiation balance and thermal dynamics.
By leveraging GSV images, this method directly characterizes urban streetscapes, including structural and geometric attributes, allowing for a comprehensive assessment. Moreover, the methods can be applied to any geographical location covered by GSV, making it a low-cost and effective tool for urban studies globally. This is particularly advantageous compared to traditional 3D-GIS models, which may not always be freely available or as extensive in coverage as GSV images.
Lessons from Street Sensing provide data-driven insights for urban planning and governance. The accurate mapping of street view factors and solar irradiance helps identify areas with insufficient greenery, excessive or insufficient solar exposure, and other urban environment issues. These insights can help policymakers and urban planners mitigate negative environmental impacts and improve urban living conditions.

Presents a new methodology that uses Google Street View images and deep learning to map and analyze urban street canyons Provides a foundation for accessible, cost-effective, data-driven strategies that can inform urban planning and design Highlights global versatility and applicability

Autorentext

Dr Gong is currently an Assistant Professor at the School of Public Administration and Policy, Renmin University of China, and a Distinguished Young Scholar of the RUC. She held Ph.D. in Architecture from The Chinese University of Hong Kong from 2014-2019 with joint building technology training by the School of Architecture and Planning, Massachusetts Institute of Technology (MIT) from 2017-2018, and was a Research Associate at the Department of Earth and Planetary Sciences, California Institute of Technology (Caltech), 2020-2021. Her research focuses on smart city and spatial governance, resilient city and environmental governance, as well as digital government and data governance. Her work has been awarded the Postgraduate Research Output Award from The Chinese University of Hong Kong (2018), the Global Excellence Research Award (2017), and the First Prize in Humanities and Social Sciences Research in Macau (2012). As a Principal Investigator, Dr. Gong has led projects funded by the National Natural Science Foundation of China, the Ministry of Education's Social Science Fund, and the Fundamental Research Funds for Central Universities. She has also been a key researcher in international projects with NASA-JPL, the Macao SAR Government, and the Hong Kong Planning Department. Her work has contributed significantly to climate-adaptive urban planning and resilient urban governance.

Inhalt

Chapter 1: Introduction.- Chapter 2: Theoretical background.- Chapter 3: Methodology.- Chapter 4: Spatial Patterns of Street Canyon View Factors.- Chapter 5: Spatiotemporal Patterns of Street Canyon Solar Radiation.- Chapter 6: Implementation of Urban Planning and Design at Street Level.- Chapter 7: Conclusions.